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Items: 1 to 20 of 116

1.

Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities.

Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, Simón-Sánchez J, Nalls MA, Keller M, Hernandez DG, Gibbs JR, Lesage S, Brice A, Heutink P, Martinez M, Wood NW, Hardy J, Singleton AB, Zell A, Gasser T, Sharma M; International Parkinson’s Disease Genomics Consortium..

Hum Mutat. 2012 Dec;33(12):1708-18. doi: 10.1002/humu.22161. Epub 2012 Aug 3.

PMID:
22777693
2.

From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

Wei Z, Wang K, Qu HQ, Zhang H, Bradfield J, Kim C, Frackleton E, Hou C, Glessner JT, Chiavacci R, Stanley C, Monos D, Grant SF, Polychronakos C, Hakonarson H.

PLoS Genet. 2009 Oct;5(10):e1000678. doi: 10.1371/journal.pgen.1000678. Epub 2009 Oct 9.

3.

Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases.

Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D, Coin L, Levin M.

PLoS One. 2009 Nov 30;4(11):e8068. doi: 10.1371/journal.pone.0008068.

4.

Risk prediction for complex diseases: application to Parkinson disease.

Hall TO, Wan JY, Mata IF, Kerr KF, Snapinn KW, Samii A, Roberts JW, Agarwal P, Zabetian CP, Edwards KL.

Genet Med. 2013 May;15(5):361-7. doi: 10.1038/gim.2012.109. Epub 2012 Dec 6.

5.

Strategies for developing prediction models from genome-wide association studies.

Wu J, Pfeiffer RM, Gail MH.

Genet Epidemiol. 2013 Dec;37(8):768-77. doi: 10.1002/gepi.21762. Epub 2013 Oct 25.

PMID:
24166696
6.

Improved risk prediction for Crohn's disease with a multi-locus approach.

Kang J, Kugathasan S, Georges M, Zhao H, Cho JH; NIDDK IBD Genetics Consortium..

Hum Mol Genet. 2011 Jun 15;20(12):2435-42. doi: 10.1093/hmg/ddr116. Epub 2011 Mar 22.

7.

Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence.

Yan J, Aliev F, Webb BT, Kendler KS, Williamson VS, Edenberg HJ, Agrawal A, Kos MZ, Almasy L, Nurnberger JI Jr, Schuckit MA, Kramer JR, Rice JP, Kuperman S, Goate AM, Tischfield JA, Porjesz B, Dick DM.

Addict Biol. 2014 Jul;19(4):708-21. doi: 10.1111/adb.12035. Epub 2013 Jan 30.

8.

Using the optimal robust receiver operating characteristic (ROC) curve for predictive genetic tests.

Lu Q, Obuchowski N, Won S, Zhu X, Elston RC.

Biometrics. 2010 Jun;66(2):586-93. doi: 10.1111/j.1541-0420.2009.01278.x. Epub 2009 Jun 8.

9.

Integrative pathway-based approach for genome-wide association studies: identification of new pathways for rheumatoid arthritis and type 1 diabetes.

Büchel F, Mittag F, Wrzodek C, Zell A, Gasser T, Sharma M.

PLoS One. 2013 Oct 25;8(10):e78577. doi: 10.1371/journal.pone.0078577. eCollection 2013.

10.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review.

Bao W, Hu FB, Rong S, Rong Y, Bowers K, Schisterman EF, Liu L, Zhang C.

Am J Epidemiol. 2013 Oct 15;178(8):1197-207. doi: 10.1093/aje/kwt123. Epub 2013 Sep 5. Review.

11.

Identifying disease related sub-pathways for analysis of genome-wide association studies.

Li C, Han J, Shang D, Li J, Wang Y, Wang Y, Zhang Y, Yao Q, Zhang C, Li K, Li X.

Gene. 2012 Jul 15;503(1):101-9. doi: 10.1016/j.gene.2012.04.051. Epub 2012 Apr 30.

PMID:
22565193
12.

The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort.

Shigemizu D, Abe T, Morizono T, Johnson TA, Boroevich KA, Hirakawa Y, Ninomiya T, Kiyohara Y, Kubo M, Nakamura Y, Maeda S, Tsunoda T.

PLoS One. 2014 Mar 20;9(3):e92549. doi: 10.1371/journal.pone.0092549. eCollection 2014.

13.

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

Mühlenbruch K, Jeppesen C, Joost HG, Boeing H, Schulze MB.

PLoS One. 2013 May 20;8(5):e64307. doi: 10.1371/journal.pone.0064307. Print 2013. Erratum in: PLoS One. 2013;8(9). doi:10.1371/annotation/65bd3a11-b821-4f10-88d2-29b69a730f21.

14.

How accurate can genetic predictions be?

Dreyfuss JM, Levner D, Galagan JE, Church GM, Ramoni MF.

BMC Genomics. 2012 Jul 24;13:340. doi: 10.1186/1471-2164-13-340.

15.

Association of type 2 diabetes GWAS loci and the risk of Parkinson's and Alzheimer's diseases.

Chung SJ, Kim MJ, Kim J, Ryu HS, Kim YJ, Kim SY, Lee JH.

Parkinsonism Relat Disord. 2015 Dec;21(12):1435-40. doi: 10.1016/j.parkreldis.2015.10.010. Epub 2015 Oct 20.

PMID:
26499758
16.

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans.

Bossé Y, Bacot F, Montpetit A, Rung J, Qu HQ, Engert JC, Polychronakos C, Hudson TJ, Froguel P, Sladek R, Desrosiers M.

Hum Genet. 2009 Apr;125(3):305-18. doi: 10.1007/s00439-009-0626-9. Epub 2009 Jan 29.

PMID:
19184112
17.

Genome-wide searching of rare genetic variants in WTCCC data.

Feng T, Zhu X.

Hum Genet. 2010 Sep;128(3):269-80. doi: 10.1007/s00439-010-0849-9. Epub 2010 Jun 13.

18.

Estimating missing heritability for disease from genome-wide association studies.

Lee SH, Wray NR, Goddard ME, Visscher PM.

Am J Hum Genet. 2011 Mar 11;88(3):294-305. doi: 10.1016/j.ajhg.2011.02.002. Epub 2011 Mar 3.

19.

Improved prediction of cardiovascular disease based on a panel of single nucleotide polymorphisms identified through genome-wide association studies.

Davies RW, Dandona S, Stewart AF, Chen L, Ellis SG, Tang WH, Hazen SL, Roberts R, McPherson R, Wells GA.

Circ Cardiovasc Genet. 2010 Oct;3(5):468-74. doi: 10.1161/CIRCGENETICS.110.946269. Epub 2010 Aug 21.

20.

Validation of type 2 diabetes risk variants identified by genome-wide association studies in Han Chinese population: a replication study and meta-analysis.

Chang YC, Liu PH, Yu YH, Kuo SS, Chang TJ, Jiang YD, Nong JY, Hwang JJ, Chuang LM.

PLoS One. 2014 Apr 15;9(4):e95045. doi: 10.1371/journal.pone.0095045. eCollection 2014.

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